garbage_stain

This is an excellent treatise on both the value of a computer science education and the importance of being sure that things you say in public aren't verifiably false.

> being attacked for mentioning possibilities.

Given the tone of what you wrote in the linked thread, I think that the redditors there showed surprising civility.

Verilog has many drawbacks, including no support for structured signals. On the other hand, the other big language, VHDL, is really difficult to use for "modular" projects. Is anyone here familiar with CHDL, a C++ hardware design language?

https://github.com/cdkersey/chdl

Being a fan of C++ myself, the idea of using template metaprogramming to represent hardware designs is something that I think is very cool.

I feel like I've read this article before.

> Now, some people may argue that these algorithms are not examples of "intelligence". The obvious conclusion must be that hiring people, beating people at Go, and playing Super Mario must also not be tasks that require intelligence.

Or it could be more nuanced than that. Effectively we have shown that given enough time (and data, and clever learning algorithms) we can teach machines to answer questions that we tell them to answer. But that is only part of intelligence---which itself is a tricky concept to even define. An intelligent agent is able to pose questions that need to be answered. (Or at least, we can probably come to a consensus agreement on that statement.)

And yes, this is a minor nit about a careless statement that was made, not an analysis of the full article.

PVS-Studio (or more specifically Andrey) used to do a really nice program called "CppHints", where they would email a little tip each day (and later each week I think).

Anyway, I guess I just wanted to say, Andrey, I appreciated your work and enjoyed reading those tips! Thanks!

mlpack has a nice command-line-accessible implementation: http://mlpack.org/docs/mlpack-2.0.3/man/mlpack_mean_shift.ht...

I had fun playing with this, but what I noticed is that the recommendations started well, but as I rated more movies, they became less and less accurate.

Is it possible that whatever technique you are using to model individual users is overfitting to noise in their ratings?

Number of stars on Github is not a great metric of the health of a project. TensorFlow, in particular, has received a lot of attention, so a lot of people have starred it, but this does not measure the number of non-Google contributors or what would happen to the project if Google abandoned it.

Note that I'm not saying TensorFlow is going to fail in the future; I'm saying that the measures used in this article are dubious at best.

Does nobody remember Elefant, Alex Smola's machine learning library that had a lot of hype then suddenly died after he left NICTA? The machine learning world is littered with tons of dead projects, and many of these died without a lot of warning. I'm concerned that this effect is only going to get worse now that machine learning libraries are often company-controlled instead of academic.

Hey, not all outdoor cats are pests! My cat is too incompetent to catch any birds and she just grazes on grass, so she's actually improving my yard by making it so I don't need to mow. Now if only I could get her to consistently eat it all to a certain height...

"Not even wrong". This entire analysis is built on reasonable statistics which are predicated on dubious and unprovable assumptions, which invalidate the entire thing.

Consider "the size of alien species". Okay... so we are extrapolating about the size of beings we know nothing about based on those beings that have come to existence in our particular situation? Assuming that the distribution of weight across animals on Earth is the same as the distribution of weight across beings in the universe is dubious.

This is a wonderful example of Brandolini's law.

Some useful links... github: https://github.com/mlpack/mlpack ; homepage: http://www.mlpack.org

P99 rocks! (just sayin')

Now here is some helpful and caring advice.

Sigh.

It was gray. You may see that I am not very active here, so I made an assumption. Today I have learned something; thank you.

This is just not a good response and does not speak to the author's problem. Yes, you can indeed say "you should be happy! It could be so much worse!" but this is not a gratifying or helpful answer to the original poster. Logically reasoning that you should feel happier about your current situation when, on an emotional level, you do not, isn't helpful. I think the other responses, which addressed the idea that all of us struggle regardless of where we are from and our current situation, is perhaps a bit more helpful.

I think it's unfortunate this has been downvoted; I think that many people feel the same way you do. It's probably worth thinking about whether your colleagues' life is really as easy as you think it is. What you see of them is probably them at their best; you probably don't see their hidden financial struggles or family struggles or professional struggles. Most people don't talk about anything like this. So personally I think it is brave to ask this question and I wish I saw topics like this discussed more often.

The project is currently in a state of transition to Github from svn, which explains the lack of issues and presence. http://www.mlpack.org/ has links to all of the old development tools such as Trac (http://www.mlpack.org/trac/) which will be ported to Github soon.